SmartCorrelatedSelection#

class feature_engine.selection.SmartCorrelatedSelection(variables=None, method='pearson', threshold=0.8, missing_values='ignore', selection_method='missing_values', estimator=None, scoring='roc_auc', cv=3, confirm_variables=False)[source]#

SmartCorrelatedSelection() finds groups of correlated features and then selects, from each group, a feature following certain criteria:

  • Feature with least missing values

  • Feature with most unique values

  • Feature with highest variance

  • Feature with highest importance according to an estimator

SmartCorrelatedSelection() returns a dataframe containing from each group of correlated features, the selected variable, plus all original features that were not correlated to any other.

Correlation is calculated with pandas.corr().

SmartCorrelatedSelection() works only with numerical variables. Categorical variables will need to be encoded to numerical or will be excluded from the analysis.

More details in the User Guide.

Parameters
variables: str or list, default=None

The list of variables to evaluate. If None, the transformer will evaluate all numerical features in the dataset.

method: string or callable, default=’pearson’

Can take ‘pearson’, ‘spearman’, ‘kendall’ or callable. It refers to the correlation method to be used to identify the correlated features.

  • ‘pearson’: standard correlation coefficient

  • ‘kendall’: Kendall Tau correlation coefficient

  • ‘spearman’: Spearman rank correlation

  • callable: callable with input two 1d ndarrays and returning a float.

For more details on this parameter visit the pandas.corr() documentation.

threshold: float, default=0.8

The correlation threshold above which a feature will be deemed correlated with another one and removed from the dataset.

missing_values: str, default=ignore

Whether the missing values should be raised as error or ignored when determining correlation. Takes values ‘raise’ and ‘ignore’.

selection_method: str, default= “missing_values”

Takes the values “missing_values”, “cardinality”, “variance” and “model_performance”.

“missing_values”: keeps the feature from the correlated group with least missing observations

“cardinality”: keeps the feature from the correlated group with the highest cardinality.

“variance”: keeps the feature from the correlated group with the highest variance.

“model_performance”: trains a machine learning model using the correlated feature group and retains the feature with the highest importance.

estimator: object

A Scikit-learn estimator for regression or classification.

scoring: str, default=’roc_auc’

Metric to evaluate the performance of the estimator. Comes from sklearn.metrics. See the model evaluation documentation for more options: https://scikit-learn.org/stable/modules/model_evaluation.html

cv: int, cross-validation generator or an iterable, default=3

Determines the cross-validation splitting strategy. Possible inputs for cv are:

For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls. For more details check Scikit-learn’s cross_validate’s documentation.

confirm_variables: bool, default=False

If set to True, variables that are not present in the input dataframe will be removed from the list of variables. Only used when passing a variable list to the parameter variables. See parameter variables for more details.

Attributes
correlated_feature_sets_:

Groups of correlated features. Each list is a group of correlated features.

features_to_drop_:

The correlated features to remove from the dataset.

variables_:

The variables that will be considered for the feature selection procedure.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

See also

pandas.corr
feature_engine.selection.DropCorrelatedFeatures

Notes

For brute-force correlation selection, check Feature-engine’s DropCorrelatedFeatures().

Methods

fit:

Find best feature from each correlated group.

fit_transform:

Fit to data, then transform it.

get_feature_names_out:

Get output feature names for transformation.

get_params:

Get parameters for this estimator.

set_params:

Set the parameters of this estimator.

transform:

Return selected features.
fit(X, y=None)[source]#

Find the correlated feature groups. Determine which feature should be selected from each group.

Parameters
X: pandas dataframe of shape = [n_samples, n_features]

The training dataset.

y: pandas series. Default = None

y is needed if selection_method == ‘model_performance’.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

input_features: None

This parameter exists only for compatibility with the Scikit-learn pipeline, but has no functionality. You can pass a list of feature names or None.

Returns
feature_names_out: list

The feature names.

:rtype:py:class:~typing.List
get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X)[source]#

Return dataframe with selected features.

Parameters
X: pandas dataframe of shape = [n_samples, n_features].

The input dataframe.

Returns
X_new: pandas dataframe of shape = [n_samples, n_selected_features]

Pandas dataframe with the selected features.

:rtype:py:class:~pandas.core.frame.DataFrame

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